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Class: Aws::SageMaker::Types::CreateProcessingJobRequest

Inherits:
Struct
  • Object
show all
Defined in:
(unknown)

Overview

Note:

When passing CreateProcessingJobRequest as input to an Aws::Client method, you can use a vanilla Hash:

{
  processing_inputs: [
    {
      input_name: "String", # required
      s3_input: { # required
        s3_uri: "S3Uri", # required
        local_path: "ProcessingLocalPath", # required
        s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix
        s3_input_mode: "Pipe", # required, accepts Pipe, File
        s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key
        s3_compression_type: "None", # accepts None, Gzip
      },
    },
  ],
  processing_output_config: {
    outputs: [ # required
      {
        output_name: "String", # required
        s3_output: { # required
          s3_uri: "S3Uri", # required
          local_path: "ProcessingLocalPath", # required
          s3_upload_mode: "Continuous", # required, accepts Continuous, EndOfJob
        },
      },
    ],
    kms_key_id: "KmsKeyId",
  },
  processing_job_name: "ProcessingJobName", # required
  processing_resources: { # required
    cluster_config: { # required
      instance_count: 1, # required
      instance_type: "ml.t3.medium", # required, accepts ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge
      volume_size_in_gb: 1, # required
      volume_kms_key_id: "KmsKeyId",
    },
  },
  stopping_condition: {
    max_runtime_in_seconds: 1, # required
  },
  app_specification: { # required
    image_uri: "ImageUri", # required
    container_entrypoint: ["ContainerEntrypointString"],
    container_arguments: ["ContainerArgument"],
  },
  environment: {
    "ProcessingEnvironmentKey" => "ProcessingEnvironmentValue",
  },
  network_config: {
    enable_inter_container_traffic_encryption: false,
    enable_network_isolation: false,
    vpc_config: {
      security_group_ids: ["SecurityGroupId"], # required
      subnets: ["SubnetId"], # required
    },
  },
  role_arn: "RoleArn", # required
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  experiment_config: {
    experiment_name: "ExperimentEntityName",
    trial_name: "ExperimentEntityName",
    trial_component_display_name: "ExperimentEntityName",
  },
}

Instance Attribute Summary collapse

Instance Attribute Details

#app_specificationTypes::AppSpecification

Configures the processing job to run a specified Docker container image.

Returns:

#environmentHash<String,String>

Sets the environment variables in the Docker container.

Returns:

  • (Hash<String,String>)

    Sets the environment variables in the Docker container.

#experiment_configTypes::ExperimentConfig

Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:

Returns:

#network_configTypes::NetworkConfig

Networking options for a processing job.

Returns:

#processing_inputsArray<Types::ProcessingInput>

For each input, data is downloaded from S3 into the processing container before the processing job begins running if \"S3InputMode\" is set to File.

Returns:

  • (Array<Types::ProcessingInput>)

    For each input, data is downloaded from S3 into the processing container before the processing job begins running if \"S3InputMode\" is set to File.

#processing_job_nameString

The name of the processing job. The name must be unique within an AWS Region in the AWS account.

Returns:

  • (String)

    The name of the processing job.

#processing_output_configTypes::ProcessingOutputConfig

Output configuration for the processing job.

Returns:

#processing_resourcesTypes::ProcessingResources

Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.

Returns:

  • (Types::ProcessingResources)

    Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job.

#role_arnString

The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

Returns:

  • (String)

    The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

#stopping_conditionTypes::ProcessingStoppingCondition

The time limit for how long the processing job is allowed to run.

Returns:

#tagsArray<Types::Tag>

(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

Returns:

  • (Array<Types::Tag>)

    (Optional) An array of key-value pairs.